Fish and health with national fish promotion programme
This is an open assessment looking at potential health effects of a national fish promotion program in Finland. The details of the assessment are described at Opasnet. This file contains the R code to run the assessment model.
Knit to html for best performance.
Calculation is based on BONUS GOHERR project and its http://en.opasnet.org/w/Goherr_assessment.
What needs to be done for PFAS assessment?
- Amount should be gender and age-specific, because we need to worry about young mothers. Solution: take KKE amounts of fish, and scale those with the Goherr subgroup-specific proportions. DONE
- Amounts should reflect the actual fish consumption in Porvoo. Solution: postpone and use national statistics. DONE
- Infant’s dioxin concentration module must be added. Solution: Use Goherr model for expo_indir DONE
- The module must be updated to match PFAS as well. Solution: update the module to contain column Exposure_agent. Change body fat parameter to volume of distribution.
- Find why expo_indir is so much higher than EFSA toxicokinetic assessment. Make an alternative model.
- Add ERF for PFAS (sum of PFOS, PFHxS, PFOA, PFNA). Solution: Make a new page for ERF of PFAS and add that to adjusted ERFs. First immunology; postpone cholesterol and low birth weight and liver toxicity. DONE
- Add case burdens for PFAS outcomes. Solution: give rough estimates for immunology to get started. DONE
- Add PFAS concentrations to data. Solution: Look at Porvoo concentrations first and make conc_pfas; combine that with conc_vit. DONE
- Add dioxin and MeHg concentrations. Later.
- Now that the model runs technically, look through each part to check that it makes sense.
# This code was forked from https://github.com/jtuomist/fishhealth/blob/master/fishhealth.Rmd
# This code was previously forked from code Op_fi5923/model on page [[Kotimaisen kalan edistämisohjelma]]
# The code was even more previously forked from Op_fi5889/model on page [[Ruori]] and Op_en7748/model on page [[Goherr assessment]]
dat <- opbase.data("Op_fi5932", subset="Malliparametrit")[-1] # [[PFAS-yhdeisteiden tautitaakka]]
dec <- opbase.data("Op_fi5932", subset="Decisions")[-1]
DecisionTableParser(dec)
CTable <- opbase.data("Op_fi5932",subset="CollapseMarginals")
#for(i in 1:ncol(CTable)) {CTable[[i]] <- as.character(CTable[[i]])} # The default is currently character, not factor
CollapseTableParser(CTable)
cat("Laskennassa käytetty data.\n")## Laskennassa käytetty data.datcat("Tarkastellut päätökset.\n")## Tarkastellut päätökset.deccat("Aggregoidut marginaalit.\n")## Aggregoidut marginaalit.CTabledummy <- Ovariable("dummy",data=data.frame(Age="dummy", Result=1))
fish_proportion <- Ovariable( # How age groups eat fish differently
"fish_proportion",
dependencies = data.frame(Name="dummy"),
formula = function(...) {
out = prepare(dat,"fish_proportion",c("Type","Exposure_agent","Response","Unit"))
out$Result <- as.numeric(as.character(out$Result))
out$Result <- out$Result / sum(out$Result) * length(out$Result)
return(out)
},
unit="-")
amount <- Ovariable(
"amount",
dependencies = data.frame(Name="fish_proportion"),
formula = function(...) {
amount = Ovariable("total_amount", data=prepare(dat, "amount", c("Type","Response","Exposure_agent","Unit")))
# Filleted weight, i.e. no loss.
amount <- amount * 1000 / 5.52 /365.25
# M kg/a per 5.52M population --> g/d per average person.
amount <- amount * fish_proportion
# fish_proportion tells the relative amount in each subgroup
# Match KKE-classification in amount with Fineli classification
tmp <- Ovariable(
output = data.frame(
Kala = c("Kasvatettu", "Kaupallinen", "Kirjolohi", "Silakka", "Vapaa-ajan", "Muu tuonti", "Tuontikirjolohi", "Tuontilohi"),
Fish = c("Whitefish", "Average fish","Rainbow trout", "Herring", "Average fish", "Average fish", "Rainbow trout", "Salmon"),
Result = 1
),
marginal = c(TRUE, TRUE, FALSE)
)
amount <- amount * tmp
return(amount)
},
unit="g/d"
)
# Exposure:To child and To eater not needed, because dioxins are not (yet) included
amount <- EvalOutput(amount)## Loading required package: reshape2##
## Attaching package: 'reshape2'## The following object is masked from 'package:tidyr':
##
## smithsggplot(amount@output, aes(x=Age, weight=amountResult, fill=Kala))+geom_bar()+
labs(
title="Kalansyönti Suomessa ikäryhmittäin",
y="Syönti (g/d)"
)
ggsave("Kalansyönti Suomessa ikäryhmittäin.svg")## Saving 7 x 5 in imagepopulation <- Ovariable(
"population",
data = prepare(dat,"population",c("Type","Exposure_agent","Response","Unit")),
unit="#")
population <- Ovariable(
"population",
data=prepare(dat, "population", c("Type", "Exposure_agent", "Response","Unit")),
unit = "#"
)
incidence <- Ovariable(
"incidence",
data = prepare(dat,"incidence",c("Type","Exposure_agent","Unit")),
unit="1/person-year")
#incidence@data$Age[is.na(incidence@data$Age)] <- ""
case_burden <- Ovariable(
"case_burden",
data = prepare(dat,"case burden",c("Type", "Exposure_agent","Unit")),
unit="DALY/case")
ERFchoice <- Ovariable(
"ERFchoice",
data =
prepare(dat, "ERFchoice", c("Unit", "Type"))
)
InpBoD <- EvalOutput(Ovariable( # Evaluated because is not a dependency but an Input
"InpBoD",
data = prepare(dat, "BoD", c("Type","Exposure_agent","Unit")),
unit="DALY/a"
))
InpBoD$Response[InpBoD$Response=="All causes"] <- "All-cause mortality"
InpBoD$Response[InpBoD$Response=="Depressive disorders"] <- "Depression"
InpBoD$Response[InpBoD$Response=="Neoplasms"] <- "Cancer morbidity"
InpBoD$Response[InpBoD$Response=="Respiratory infections and tuberculosis"] <- "Immunosuppression" # Infections of 0-9-year-olds are assumed to represent the background BoD of immunosuppressive effect of PFAS
InpBoD$Response[InpBoD$Response=="Cardiovascular diseases"] <- "CHD2 mortality"conc_vit <- Ovariable(
"conc_vit",
ddata = "Op_en1838", # [[Concentrations of beneficial nutrients in fish]]
subset = "Fineli data for common fish species"
)
df = conc_vit@data
df$Nutrient[df$Nutrient=="D-vitamiini (µg)"] <- "Vitamin D"
df$Nutrient[df$Nutrient=="rasvahapot n-3 moni-tyydyttymättömät (g)"] <- "Omega3"
df$Nutrient[df$Nutrient=="rasvahappo 18:3 n-3 (alfalinoleenihappo) (mg)"] <- "ALA"
df$Nutrient[df$Nutrient=="rasvahappo 22:6 n-3 (DHA) (mg)"] <- "DHA"
df$Nutrient[df$Nutrient=="proteiini (g)"] <- "Fish"
df$conc_vitResult[df$Nutrient=="Fish"] <- "1"
df <- dropall(df[df$Nutrient %in% c("Vitamin D", "Omega3", "ALA", "DHA", "Fish") , ])
conc_vit@data <- df
######## Concentration of PFAS
# Data from EU-kalat3 (Finland excl Vanhankaupunginlahti): # pg/g fresh weight
# POP mean sd min Q0.025 median Q0.975 max
# 2.5% PFOS 2055.757 1404.045 305.0399 330.1365 1533.269 5029.697 5814.935
# Data from EU-kalat3 (Vanhankaupunginlahti, Helsinki) # ng/g f.w.
# POP mean sd min Q0.025 median Q0.975 max
#2.5% PFOS 14.428 11.94542 1.499441 1.607789 15.64988 35.32517 38.91994
conc_eukalat <- EvalOutput(Ovariable(
"conc_eukalat",
data = data.frame(
Area = c("Suomi","Helsinki"),
Compound="PFOS",
Result=c("2.056 (3.301 - 5.030)", "14.428 (1.499 - 35.325)")),
unit="ng/g fresh weight"
))
conc_pfas <- Ovariable(
"conc_pfas",
data=opbase.data("Op_fi5932",subset="PFAS concentrations"),
unit="ng/g fresh weight")
conc_pfas@data$Area <- "Porvoo"
conc_pfas <- EvalOutput(conc_pfas)
ggplot(conc_pfas@output, aes(x=conc_pfasResult, color=Compound, linetype=Area))+stat_ecdf()+
scale_x_log10()+
stat_ecdf(data=conc_eukalat@output, aes(x=conc_eukalatResult))+
scale_linetype_manual(values=c("dotted","solid","twodash"))+
labs(
title="PFAS concentration in fishes in Finland",
x="PFAS concentration (ng/g fresh weight)",
y="Cumulative probability"
)
# The code may produce some negative values, which are removed from the graph
ggsave("PFAS-pitoisuus kalassa Suomessa.svg")## Saving 7 x 5 in imagesum_pfas <- oapply(conc_pfas, cols=c("Kala","Compound"), FUN=sum)
tmp <- conc_pfas / sum_pfas
summary(tmp, marginals="Compound")## This tells that PFOS consists of 71 - 97 % of the four key PFAS, while PFOA, PFNA, and PFHxS consist of
# 0 - 10 %, 2 - 18 %, and 0 - 9 %, respectively.
# Even if we included the next most abundant congeners, i.e. PFDA and PFUnA, the overall picture would not change.
conc <- Ovariable(
"conc",
dependencies = data.frame(Name="conc_vit", "conc_pfas"),
formula = function(...){
conc_vit <- oapply(conc_vit, cols=c("Kala", "Adjust"),FUN=mean)
colnames(conc_vit@output)[colnames(conc_vit@output)=="Nutrient"] <- "Compound"
conc_pfas <- oapply(conc_pfas, cols=c("Obs","Area"), FUN=mean)
conc_pfas$Compound[conc_pfas$Compound %in% c("PFOA","PFNA","PFHxS","PFOS")] <- "PFAS"
conc_pfas <- oapply(conc_pfas, cols="", FUN=sum)
out <- OpasnetUtils::combine(conc_vit, conc_pfas)
return(out)
}
)
conc <- EvalOutput(conc)
ggplot(conc@output, aes(x=concResult, colour=Fish))+stat_ecdf()+
facet_wrap(~Compound, scales="free_x")
###################################################################
# Code copied from http://en.opasnet.org/w/Goherr_assessment#
mc2dparam<- list(
N2 = 10, # Number of iterations in the new Iter
strength = 50, # Sample size to which the fun is to be applied. Resembles number of observations
run2d = FALSE, # Should the mc2d function be used or not?
info = 1, # Ovariable that contains additional indices, e.g. newmarginals.
newmarginals = c("Group","Exposure"), # Names of columns that are non-marginals but should be sampled enough to become marginals
method = "bootstrap", # which method to use for 2D Monte Carlo? Currently bootsrap is the only option.
fun = mean # Function for aggregating the first Iter dimension.
)
if(FALSE) {
## Exposure with background exposure but without mother's exposure to child
expo_dir <- Ovariable(
"expo_dir",
dependencies=data.frame(Name=c("amount","conc","expo_bg")),
formula = function(...) {
out <- conc[conc$Exposure_agent=="TEQ",] * 0 + 1
out$Exposure_agent <- "Fish"
out <- combine(conc, out, name="conc")
out <- oapply(amount * out, cols="Fish", FUN=sum)
out <- Ovariable(output = data.frame(
Exposcen = c("BAU", "No exposure"),
Result = c(1, 0)
), marginal=c(TRUE,FALSE)) * out + expo_bg
out$Exposure <- as.factor(
ifelse(
out$Exposure_agent %in% c("DHA", "MeHg"),
"To child",
"To eater"
)
)
return(out)
},
unit = "PCDDF, PCB, TEQ: pg /d; Vitamin D, MeHg: µg /d; DHA, EPA, Omega3: mg /d; Fish: g /d"
)
## Background-exposure to vitamin D and omega-3
addexposure <- Ovariable(
"addexposure",
ddata = "Op_en7748", # [[Benefit-risk assessment of Baltic herring and salmon intake]]
subset = "Background exposure",
unit = "PCDDF, PCB, TEQ: pg /d; Vitamin D, MeHg: µg /d; DHA, EPA, Omega3: mg /d"
)
# Should the background be specific for gender and country? At the moment it is.
expo_bg <- Ovariable(
"expo_bg",
dependencies = data.frame(Name="addexposure","info"),
formula = function(...) {
out <- addexposure
# Empty values ("") in indices must be replaced by NA so that Ops works correctly.
levels(out$Gender)[levels(out$Gender) == ""] <- NA
levels(out$Country)[levels(out$Country) == ""] <- NA
levels(out$Exposure_agent)[levels(out$Exposure_agent) == ""] <- NA
out@output <- fillna(out@output, c("Country", "Gender", "Exposure_agent"))
temp1 <- out[out$Exposure_agent %in% c("PCDDF","PCB") , ]
temp1 <- oapply(temp1, cols = "Exposure_agent", FUN = sum)
temp1$Exposure_agent <- "TEQ"
temp2 <- out[out$Exposure_agent %in% c("EPA", "DHA") , ]
temp2 <- oapply(temp2, cols = "Exposure_agent", FUN = sum)
temp2$Exposure_agent <- "Omega3"
out <- combine(out, temp1, temp2)
out <- unkeep(out * info, prevresults = TRUE, sources = TRUE)
return(out)
},
unit = "PCDDF, PCB, TEQ: pg /d; Vitamin D, MeHg: µg /d; DHA, EPA, Omega3: mg /d"
)
# Stores non-marginal columns for further use.
info <- Ovariable(
"info",
dependencies = data.frame(Name = c("jsp")),
formula = function(...) {
out <- jsp
out$Group <- factor(
paste(out$Gender, out$Ages),
levels = c("Female 18-45", "Male 18-45", "Female >45", "Male >45")
)
out$Country <- factor(out$Country, ordered=FALSE)
out <- unique(out@output[c("Iter","Country","Group","Gender","Row")])
out$Result <- 1
return(out)
}
)
} # END IF
############################### Code from Goherr assessment ends
expo_dir <- Ovariable(
"expo_dir",
dependencies = data.frame(Name=c("conc", "amount")),
formula = function(...) {
conc$Fish[conc$Fish %in% c("Perch","Pike-perch","Eel","")] <- "Average fish"
conc <- oapply(conc, cols="", FUN=mean)
out <- conc * amount
# colnames(out@output)[colnames(out@output)=="Nutrient"] <- "Exposure_agent"
return(out)
},
unit="ng/d"
)
expo_dir <- EvalOutput(expo_dir)
#View(expo_dir@output)
ggplot(oapply(expo_dir, cols=c("Iter"),FUN=mean)@output,
aes(x=Age, weight=expo_dirResult,fill=Fish))+geom_bar()+
facet_wrap(~Compound, scales="free_y")+
labs(title="Eri yhdisteiden saanti kalasta")
ggsave("Yhdisteiden saanti kalasta Suomessa.svg")## Saving 7 x 5 in imageexposure <- Ovariable(
"exposure",
dependencies = data.frame(
Name = c(
"expo_dir", # direct exposure, i.e. the person eats or breaths the exposure agent themself
"expo_indir", # indirect exposure, i.e. the person (typically fetus or infant) is exposed via someone else (mother)
"mc2d" # 2D Monte Carlo function
),
Ident = c(
NA,
"Op_en7797/expo_indir2", # [[Infant's dioxin exposure]] # expo_indir
"Op_en7805/mc2d") # [[Two-dimensional Monte Carlo]]
),
formula = function(...) {
out <- OpasnetUtils::combine(expo_dir, expo_indir)
out <- unkeep(out, "Source.1", sources=TRUE)
out <- mc2d(out)
out$Exposure[is.na(out$Exposure)] <- "Direct"
return(out)
},
unit = "PCDDF, PCB, TEQ: (To eater: pg /day; to child: pg /g fat); Vitamin D, MeHg: µg /day; DHA, EPA, Omega3: mg /day"
)
exposure <- Ovariable("exposure", data=data.frame(Result=1))
if(FALSE) {
exposure <- EvalOutput(exposure)
View(exposure@output)
ggplot(exposure@output, aes(x=Age, weight=exposureResult, fill=Fish))+geom_bar()+
facet_grid(Compound~Exposure, scales="free_y")+
labs(
title="Exposure to compounds",
y="(omega: mg/d; vit D: ug/d, PFAS: ng/d)"
)
} # END IFobjects.latest("Op_en2261",code_name="BoDattr2") # [[Health impact assessment]]## Loading objects:
## BoDattrtryCatch(BoDattr <- EvalOutput(BoDattr, verbose=TRUE))## Evaluating BoDattr ...
##
## - BoD fetched successfully!
##
## - PAF fetched successfully!
## - Evaluating BoD ...
## - - Evaluating incidence ...
##
## done(0 secs)!
## - - Checking incidence marginals ... Response, Age, incidenceSource recognized as marginal(s).
## - - Processing incidence decisions ... done!
## - - Evaluating case_burden ...
##
## done(0 secs)!
## - - Checking case_burden marginals ... Response, case_burdenSource recognized as marginal(s).
## - - Processing case_burden marginal collapses ... done!
## - - Evaluating population ...
##
## done(0.01 secs)!
## - - Checking population marginals ... Gender, Age, populationSource recognized as marginal(s).
##
## - done(0.19 secs)!
## - Checking BoD marginals ... Response, Age, incidenceSource, Adjust, Gender, populationSource, BoDSource recognized as marginal(s).
## - Processing BoD inputs ... done!
## - Processing BoD marginal collapses ...## Warning in oapply(variable, FUN = fun[[i]], cols = cols[[i]], na.rm = TRUE):
## While oapplying BoD, found NAs in indices: Adjust, InpBoDSource. They were
## automatically filled using fillna, which may result in a multiplied population.
## Please check your ovariable before using oapply.## done!
## - Evaluating PAF ...
##
## - - dose fetched successfully!
##
## - - ERF fetched successfully!
##
## - - RR fetched successfully!
##
## - - frexposed fetched successfully!
##
## - - P_illness fetched successfully!
##
## - - sumExposcen fetched successfully!
##
## - - mc2d fetched successfully!
## - - Evaluating dose ...
##
## - - - BW fetched successfully!
## - - - Evaluating exposure ...
##
## done(0 secs)!
## - - - Checking exposure marginals ... exposureSource recognized as marginal(s).
## - - - Processing exposure marginal collapses ... done!
## - - - Evaluating BW ...
##
## done(0 secs)!
## - - - Checking BW marginals ... BWSource recognized as marginal(s).
##
## -- done(25.17 secs)!
## - - Checking dose marginals ... Scaling, exposureSource, BWSource, doseSource recognized as marginal(s).
## - - Processing dose marginal collapses ... done!
## - - Evaluating ERF ...
##
## - - - ERF_env fetched successfully!
##
## - - - ERF_omega3 fetched successfully!
##
## - - - ERF_mehg fetched successfully!
##
## - - - ERF_diox fetched successfully!
##
## - - - ERF_vit fetched successfully!
##
## - - - ERF_micr fetched successfully!
##
## - - - ERF_pfas fetched successfully!
## - - - Evaluating ERF_env ...
##
## done(0.03 secs)!
## - - - Checking ERF_env marginals ... Exposure_agent, Response, Subgroup, Exposure, ER_function, Scaling, Exposure_unit, Observation, ERF_envSource recognized as marginal(s).
## - - - Evaluating ERF_omega3 ...
##
## done(0 secs)!
## - - - Checking ERF_omega3 marginals ... Exposure_agent, Response, Exposure, Exposure_unit, ER_function, Scaling, Observation, ERF_omega3Source recognized as marginal(s).
## - - - Evaluating ERF_mehg ...
##
## done(0 secs)!
## - - - Checking ERF_mehg marginals ... Exposure_agent, Response, Exposure, Exposure_unit, ER_function, Scaling, Observation, ERF_mehgSource recognized as marginal(s).
## - - - Evaluating ERF_diox ...
##
## done(0 secs)!
## - - - Checking ERF_diox marginals ... Exposure_agent, Response, Exposure, Exposure_unit, ER_function, Scaling, Observation, ERF_dioxSource recognized as marginal(s).
## - - - Evaluating ERF_vit ...
##
## done(0 secs)!
## - - - Checking ERF_vit marginals ... Exposure_agent, Response, Exposure, Exposure_unit, ER_function, Scaling, Observation, ERF_vitSource recognized as marginal(s).
## - - - Evaluating ERF_micr ...
##
## done(0 secs)!
## - - - Checking ERF_micr marginals ... Exposure_agent, Response, Exposure, Exposure_unit, ER_function, Scaling, Observation, ERF_micrSource recognized as marginal(s).
## - - - Evaluating ERF_pfas ...
##
## done(0 secs)!
## - - - Checking ERF_pfas marginals ... Exposure_agent, Response, Exposure, Exposure_unit, ER_function, Scaling, Observation, ERF_pfasSource recognized as marginal(s).
## - - - Evaluating ERFchoice ...
##
## done(0 secs)!
## - - - Checking ERFchoice marginals ... Exposure_agent, Response, Scaling, Exposure, ER_function, ERFchoiceSource recognized as marginal(s).
##
## -- done(3.02 mins)!
## - - Checking ERF marginals ... Exposure_agent, Response, Exposure, ER_function, Scaling, Observation, ERFSource recognized as marginal(s).
## - - Processing ERF marginal collapses ... done!
## - - Evaluating RR ...
## - - - Processing dose marginal collapses ... done!
## - - - Processing ERF marginal collapses ... done!
##
## -- done(0.16 secs)!
## - - Checking RR marginals ... Exposure_agent, Response, ER_function, Scaling, ERFSource, doseSource, RRSource recognized as marginal(s).
## - - Evaluating frexposed ...
##
## done(0 secs)!
## - - Checking frexposed marginals ... frexposedSource recognized as marginal(s).
## - - Evaluating P_illness ...
##
## done(0 secs)!
## - - Checking P_illness marginals ... Response, Illness, Age, P_illnessSource recognized as marginal(s).
##
## - done(6.6 mins)!
## - Checking PAF marginals ... Exposure_agent, Response, ER_function, Scaling, ERFSource, doseSource, frexposedSource, Age, incidenceSource, Adjust, RRSource, PAFSource recognized as marginal(s).
## - Processing PAF marginal collapses ...## Warning in oapply(variable, FUN = fun[[i]], cols = cols[[i]], na.rm = TRUE):
## While oapplying PAF, found NAs in indices: Age, Adjust. They were automatically
## filled using fillna, which may result in a multiplied population. Please check
## your ovariable before using oapply.## done!
##
## done(7.44 mins)!
## Checking BoDattr marginals ... Response, Age, Gender, Adjust, InpBoDSource, Exposure_agent, PAFSource, BoDattrSource recognized as marginal(s).oprint(summary(amount,"mean"))## Kala Scenario Age Fish mean
## 1 Kaupallinen BAU Female 18-45 Average fish 0.7187139
## 2 Muu tuonti BAU Female 18-45 Average fish 6.2887467
## 3 Vapaa-ajan BAU Female 18-45 Average fish 2.2235211
## 4 Kaupallinen BAU Female 45+ Average fish 1.7967848
## 5 Muu tuonti BAU Female 45+ Average fish 15.7218667
## 6 Vapaa-ajan BAU Female 45+ Average fish 5.5588029
## 7 Kaupallinen BAU Non Female 18-45 Average fish 1.4374278
## 8 Muu tuonti BAU Non Female 18-45 Average fish 12.5774934
## 9 Vapaa-ajan BAU Non Female 18-45 Average fish 4.4470423
## 10 Kaupallinen BAU Non Female 45+ Average fish 2.3957130
## 11 Muu tuonti BAU Non Female 45+ Average fish 20.9624889
## 12 Vapaa-ajan BAU Non Female 45+ Average fish 7.4117372
## 13 Silakka BAU Female 18-45 Herring 0.3593570
## 14 Silakka BAU Female 45+ Herring 0.8983924
## 15 Silakka BAU Non Female 18-45 Herring 0.7187139
## 16 Silakka BAU Non Female 45+ Herring 1.1978565
## 17 Kirjolohi BAU Female 18-45 Rainbow trout 1.5048072
## 18 Tuontikirjolohi BAU Female 18-45 Rainbow trout 1.0780709
## 19 Kirjolohi BAU Female 45+ Rainbow trout 3.7620181
## 20 Tuontikirjolohi BAU Female 45+ Rainbow trout 2.6951771
## 21 Kirjolohi BAU Non Female 18-45 Rainbow trout 3.0096145
## 22 Tuontikirjolohi BAU Non Female 18-45 Rainbow trout 2.1561417
## 23 Kirjolohi BAU Non Female 45+ Rainbow trout 5.0160241
## 24 Tuontikirjolohi BAU Non Female 45+ Rainbow trout 3.5935695
## 25 Tuontilohi BAU Female 18-45 Salmon 5.3005151
## 26 Tuontilohi BAU Female 45+ Salmon 13.2512876
## 27 Tuontilohi BAU Non Female 18-45 Salmon 10.6010301
## 28 Tuontilohi BAU Non Female 45+ Salmon 17.6683835
## 29 Kasvatettu BAU Female 18-45 Whitefish 0.1347589
## 30 Kasvatettu BAU Female 45+ Whitefish 0.3368971
## 31 Kasvatettu BAU Non Female 18-45 Whitefish 0.2695177
## 32 Kasvatettu BAU Non Female 45+ Whitefish 0.4491962oprint(summary(BoD,"mean"))## Response Age Gender Adjust
## 1 Loss in child's IQ points 0 - 4 Female BAU
## 2 Sperm concentration 0 - 4 Female BAU
## 3 Yes or no dental defect 0 - 4 Female BAU
## 4 All-cause mortality 0 - 4 Female BAU
## 5 Cancer morbidity 0 - 4 Female BAU
## 6 Depression 0 - 4 Female BAU
## 7 CHD2 mortality 0 - 4 Female BAU
## 8 Immunosuppression 0 - 4 Female BAU
## 9 Dioxin recommendation tolerable daily intake Undefined Female BAU
## 10 Dioxin recommendation tolerable daily intake 2018 Undefined Female BAU
## 11 PFAS TWI Undefined Female BAU
## 12 Vitamin D recommendation Undefined Female BAU
## 13 All-cause mortality 5 - 9 Female BAU
## 14 Cancer morbidity 5 - 9 Female BAU
## 15 Depression 5 - 9 Female BAU
## 16 CHD2 mortality 5 - 9 Female BAU
## 17 Immunosuppression 5 - 9 Female BAU
## 18 All-cause mortality 10 - 14 Female BAU
## 19 Cancer morbidity 10 - 14 Female BAU
## 20 Depression 10 - 14 Female BAU
## 21 CHD2 mortality 10 - 14 Female BAU
## 22 All-cause mortality 15 - 19 Female BAU
## 23 Cancer morbidity 15 - 19 Female BAU
## 24 Depression 15 - 19 Female BAU
## 25 CHD2 mortality 15 - 19 Female BAU
## 26 Breast cancer 15 - 19 Female BAU
## 27 All-cause mortality 20 - 24 Female BAU
## 28 Cancer morbidity 20 - 24 Female BAU
## 29 Depression 20 - 24 Female BAU
## 30 CHD2 mortality 20 - 24 Female BAU
## 31 Breast cancer 20 - 24 Female BAU
## 32 All-cause mortality 25 - 29 Female BAU
## 33 Cancer morbidity 25 - 29 Female BAU
## 34 Depression 25 - 29 Female BAU
## 35 CHD2 mortality 25 - 29 Female BAU
## 36 Breast cancer 25 - 29 Female BAU
## 37 All-cause mortality 30 - 34 Female BAU
## 38 Cancer morbidity 30 - 34 Female BAU
## 39 Depression 30 - 34 Female BAU
## 40 CHD2 mortality 30 - 34 Female BAU
## 41 Breast cancer 30 - 34 Female BAU
## 42 All-cause mortality 35 - 39 Female BAU
## 43 Cancer morbidity 35 - 39 Female BAU
## 44 Depression 35 - 39 Female BAU
## 45 CHD2 mortality 35 - 39 Female BAU
## 46 Breast cancer 35 - 39 Female BAU
## 47 All-cause mortality 40 - 44 Female BAU
## 48 Cancer morbidity 40 - 44 Female BAU
## 49 Depression 40 - 44 Female BAU
## 50 CHD2 mortality 40 - 44 Female BAU
## 51 Breast cancer 40 - 44 Female BAU
## 52 All-cause mortality 45 - 49 Female BAU
## 53 Cancer morbidity 45 - 49 Female BAU
## 54 Depression 45 - 49 Female BAU
## 55 CHD2 mortality 45 - 49 Female BAU
## 56 Breast cancer 45 - 49 Female BAU
## 57 All-cause mortality 50 - 54 Female BAU
## 58 Cancer morbidity 50 - 54 Female BAU
## 59 Depression 50 - 54 Female BAU
## 60 CHD2 mortality 50 - 54 Female BAU
## 61 Breast cancer 50 - 54 Female BAU
## 62 All-cause mortality 55 - 59 Female BAU
## 63 Cancer morbidity 55 - 59 Female BAU
## 64 Depression 55 - 59 Female BAU
## 65 CHD2 mortality 55 - 59 Female BAU
## 66 Breast cancer 55 - 59 Female BAU
## 67 All-cause mortality 60 - 64 Female BAU
## 68 Cancer morbidity 60 - 64 Female BAU
## 69 Depression 60 - 64 Female BAU
## 70 CHD2 mortality 60 - 64 Female BAU
## 71 Breast cancer 60 - 64 Female BAU
## 72 All-cause mortality 65 - 69 Female BAU
## 73 Cancer morbidity 65 - 69 Female BAU
## 74 Depression 65 - 69 Female BAU
## 75 CHD2 mortality 65 - 69 Female BAU
## 76 Breast cancer 65 - 69 Female BAU
## 77 All-cause mortality 70 - 74 Female BAU
## 78 Cancer morbidity 70 - 74 Female BAU
## 79 Depression 70 - 74 Female BAU
## 80 CHD2 mortality 70 - 74 Female BAU
## 81 Breast cancer 70 - 74 Female BAU
## 82 All-cause mortality 75 - 79 Female BAU
## 83 Cancer morbidity 75 - 79 Female BAU
## 84 Depression 75 - 79 Female BAU
## 85 CHD2 mortality 75 - 79 Female BAU
## 86 Breast cancer 75 - 79 Female BAU
## 87 All-cause mortality 80 - 84 Female BAU
## 88 Cancer morbidity 80 - 84 Female BAU
## 89 Depression 80 - 84 Female BAU
## 90 CHD2 mortality 80 - 84 Female BAU
## 91 Breast cancer 80 - 84 Female BAU
## 92 All-cause mortality 85 - 89 Female BAU
## 93 Cancer morbidity 85 - 89 Female BAU
## 94 Depression 85 - 89 Female BAU
## 95 CHD2 mortality 85 - 89 Female BAU
## 96 Breast cancer 85 - 89 Female BAU
## 97 All-cause mortality 90 - 94 Female BAU
## 98 Cancer morbidity 90 - 94 Female BAU
## 99 Depression 90 - 94 Female BAU
## 100 CHD2 mortality 90 - 94 Female BAU
## 101 Breast cancer 90 - 94 Female BAU
## 102 Loss in child's IQ points 0 - 4 Male BAU
## 103 Sperm concentration 0 - 4 Male BAU
## 104 Yes or no dental defect 0 - 4 Male BAU
## 105 All-cause mortality 0 - 4 Male BAU
## 106 Cancer morbidity 0 - 4 Male BAU
## 107 Depression 0 - 4 Male BAU
## 108 CHD2 mortality 0 - 4 Male BAU
## 109 Immunosuppression 0 - 4 Male BAU
## 110 Dioxin recommendation tolerable daily intake Undefined Male BAU
## 111 Dioxin recommendation tolerable daily intake 2018 Undefined Male BAU
## 112 PFAS TWI Undefined Male BAU
## 113 Vitamin D recommendation Undefined Male BAU
## 114 All-cause mortality 5 - 9 Male BAU
## 115 Cancer morbidity 5 - 9 Male BAU
## 116 Depression 5 - 9 Male BAU
## 117 CHD2 mortality 5 - 9 Male BAU
## 118 Immunosuppression 5 - 9 Male BAU
## 119 All-cause mortality 10 - 14 Male BAU
## 120 Cancer morbidity 10 - 14 Male BAU
## 121 Depression 10 - 14 Male BAU
## 122 CHD2 mortality 10 - 14 Male BAU
## 123 All-cause mortality 15 - 19 Male BAU
## 124 Cancer morbidity 15 - 19 Male BAU
## 125 Depression 15 - 19 Male BAU
## 126 CHD2 mortality 15 - 19 Male BAU
## 127 Breast cancer 15 - 19 Male BAU
## 128 All-cause mortality 20 - 24 Male BAU
## 129 Cancer morbidity 20 - 24 Male BAU
## 130 Depression 20 - 24 Male BAU
## 131 CHD2 mortality 20 - 24 Male BAU
## 132 Breast cancer 20 - 24 Male BAU
## 133 All-cause mortality 25 - 29 Male BAU
## 134 Cancer morbidity 25 - 29 Male BAU
## 135 Depression 25 - 29 Male BAU
## 136 CHD2 mortality 25 - 29 Male BAU
## 137 Breast cancer 25 - 29 Male BAU
## 138 All-cause mortality 30 - 34 Male BAU
## 139 Cancer morbidity 30 - 34 Male BAU
## 140 Depression 30 - 34 Male BAU
## 141 CHD2 mortality 30 - 34 Male BAU
## 142 Breast cancer 30 - 34 Male BAU
## 143 All-cause mortality 35 - 39 Male BAU
## 144 Cancer morbidity 35 - 39 Male BAU
## 145 Depression 35 - 39 Male BAU
## 146 CHD2 mortality 35 - 39 Male BAU
## 147 Breast cancer 35 - 39 Male BAU
## 148 All-cause mortality 40 - 44 Male BAU
## 149 Cancer morbidity 40 - 44 Male BAU
## 150 Depression 40 - 44 Male BAU
## 151 CHD2 mortality 40 - 44 Male BAU
## 152 Breast cancer 40 - 44 Male BAU
## 153 All-cause mortality 45 - 49 Male BAU
## 154 Cancer morbidity 45 - 49 Male BAU
## 155 Depression 45 - 49 Male BAU
## 156 CHD2 mortality 45 - 49 Male BAU
## 157 Breast cancer 45 - 49 Male BAU
## 158 All-cause mortality 50 - 54 Male BAU
## 159 Cancer morbidity 50 - 54 Male BAU
## 160 Depression 50 - 54 Male BAU
## 161 CHD2 mortality 50 - 54 Male BAU
## 162 Breast cancer 50 - 54 Male BAU
## 163 All-cause mortality 55 - 59 Male BAU
## 164 Cancer morbidity 55 - 59 Male BAU
## 165 Depression 55 - 59 Male BAU
## 166 CHD2 mortality 55 - 59 Male BAU
## 167 Breast cancer 55 - 59 Male BAU
## 168 All-cause mortality 60 - 64 Male BAU
## 169 Cancer morbidity 60 - 64 Male BAU
## 170 Depression 60 - 64 Male BAU
## 171 CHD2 mortality 60 - 64 Male BAU
## 172 Breast cancer 60 - 64 Male BAU
## 173 All-cause mortality 65 - 69 Male BAU
## 174 Cancer morbidity 65 - 69 Male BAU
## 175 Depression 65 - 69 Male BAU
## 176 CHD2 mortality 65 - 69 Male BAU
## 177 Breast cancer 65 - 69 Male BAU
## 178 All-cause mortality 70 - 74 Male BAU
## 179 Cancer morbidity 70 - 74 Male BAU
## 180 Depression 70 - 74 Male BAU
## 181 CHD2 mortality 70 - 74 Male BAU
## 182 Breast cancer 70 - 74 Male BAU
## 183 All-cause mortality 75 - 79 Male BAU
## 184 Cancer morbidity 75 - 79 Male BAU
## 185 Depression 75 - 79 Male BAU
## 186 CHD2 mortality 75 - 79 Male BAU
## 187 Breast cancer 75 - 79 Male BAU
## 188 All-cause mortality 80 - 84 Male BAU
## 189 Cancer morbidity 80 - 84 Male BAU
## 190 Depression 80 - 84 Male BAU
## 191 CHD2 mortality 80 - 84 Male BAU
## 192 Breast cancer 80 - 84 Male BAU
## 193 All-cause mortality 85 - 89 Male BAU
## 194 Cancer morbidity 85 - 89 Male BAU
## 195 Depression 85 - 89 Male BAU
## 196 CHD2 mortality 85 - 89 Male BAU
## 197 Breast cancer 85 - 89 Male BAU
## 198 All-cause mortality 90 - 94 Male BAU
## 199 Cancer morbidity 90 - 94 Male BAU
## 200 Depression 90 - 94 Male BAU
## 201 CHD2 mortality 90 - 94 Male BAU
## 202 Breast cancer 90 - 94 Male BAU
## mean
## 1 16395.2448
## 2 4376.4000
## 3 336.1075
## 4 4367.2700
## 5 279.4400
## 6 0.4400
## 7 51.4100
## 8 272.0400
## 9 309.2078
## 10 899.5137
## 11 2810.9804
## 12 618.4157
## 13 909.3800
## 14 352.6300
## 15 87.5700
## 16 57.2800
## 17 248.0400
## 18 1012.2200
## 19 357.1200
## 20 680.2900
## 21 87.0900
## 22 1945.6100
## 23 386.2000
## 24 1657.4000
## 25 153.9800
## 26 3.0000
## 27 2684.3600
## 28 524.2800
## 29 2295.7100
## 30 228.8100
## 31 14.4000
## 32 2980.0200
## 33 728.7600
## 34 2185.6700
## 35 342.4600
## 36 72.4600
## 37 3569.3800
## 38 1173.6800
## 39 1876.2400
## 40 456.4700
## 41 289.2600
## 42 4372.6700
## 43 1750.8000
## 44 2009.3100
## 45 655.6500
## 46 525.1700
## 47 5831.2700
## 48 2660.2800
## 49 1980.0500
## 50 1017.7100
## 51 863.5600
## 52 8481.7500
## 53 4204.3800
## 54 1805.0000
## 55 1511.8900
## 56 1403.4500
## 57 13751.6900
## 58 7197.9400
## 59 2050.3500
## 60 2418.4000
## 61 2079.3300
## 62 19024.8900
## 63 10372.2500
## 64 2139.7100
## 65 3830.7900
## 66 2667.9900
## 67 25603.4700
## 68 14317.8700
## 69 2050.2600
## 70 6174.0400
## 71 3008.1400
## 72 34094.4500
## 73 18551.3000
## 74 1910.2400
## 75 10009.4600
## 76 3280.9700
## 77 44484.9600
## 78 21707.2100
## 79 1738.8100
## 80 16202.4200
## 81 3328.9100
## 82 41490.0000
## 83 15942.7400
## 84 992.1300
## 85 18996.6800
## 86 2448.5100
## 87 51345.4900
## 88 13735.8800
## 89 754.1900
## 90 28299.7900
## 91 1889.1800
## 92 49589.0500
## 93 8476.7900
## 94 508.9600
## 95 30471.0100
## 96 1151.1600
## 97 36317.0200
## 98 4083.6700
## 99 282.5900
## 100 23285.7800
## 101 589.9300
## 102 17161.5101
## 103 4580.9400
## 104 351.8162
## 105 5367.6600
## 106 339.6000
## 107 0.3900
## 108 55.8100
## 109 319.9300
## 110 301.6056
## 111 877.3982
## 112 2741.8694
## 113 603.2113
## 114 952.8700
## 115 338.4100
## 116 65.4000
## 117 40.2800
## 118 275.0300
## 119 1158.8700
## 120 337.0300
## 121 431.4800
## 122 63.5400
## 123 3986.2800
## 124 443.7500
## 125 984.2500
## 126 136.7100
## 127 1.1800
## 128 7865.3300
## 129 614.7700
## 130 1386.3200
## 131 279.3900
## 132 1.1700
## 133 9547.0600
## 134 862.1500
## 135 1417.2600
## 136 532.8300
## 137 1.1800
## 138 9851.2400
## 139 1122.2900
## 140 1303.8000
## 141 816.3700
## 142 1.0600
## 143 12178.6200
## 144 1607.4500
## 145 1434.1400
## 146 1680.8700
## 147 2.0300
## 148 14632.7600
## 149 2244.8500
## 150 1420.5100
## 151 2937.4200
## 152 3.1400
## 153 18900.8100
## 154 3494.3800
## 155 1263.3400
## 156 4722.7300
## 157 6.4600
## 158 28892.0400
## 159 6777.8600
## 160 1387.5600
## 161 8534.1800
## 162 11.3200
## 163 40698.9300
## 164 12102.4100
## 165 1394.6200
## 166 13664.1400
## 167 15.6400
## 168 52798.2800
## 169 18345.9500
## 170 1303.2200
## 171 19981.9000
## 172 18.2900
## 173 64496.5600
## 174 25248.0000
## 175 1212.9100
## 176 26192.0400
## 177 17.6900
## 178 75114.4400
## 179 29396.5100
## 180 1113.6600
## 181 32896.1500
## 182 27.1600
## 183 57367.0300
## 184 20087.7200
## 185 607.2700
## 186 27830.2900
## 187 15.5100
## 188 52592.3400
## 189 15565.9500
## 190 408.4000
## 191 27498.8800
## 192 12.5800
## 193 35941.6400
## 194 8349.3200
## 195 217.1900
## 196 20009.0600
## 197 5.2200
## 198 17196.2600
## 199 2990.6800
## 200 89.6400
## 201 9975.4500
## 202 2.4300oprint(summary(BoDattr,"mean"))## Response Age Gender Adjust
## 1 Loss in child's IQ points 0 - 4 Female BAU
## 2 Loss in child's IQ points 0 - 4 Male BAU
## 3 Loss in child's IQ points 0 - 4 Female BAU
## 4 Loss in child's IQ points 0 - 4 Male BAU
## 5 Sperm concentration 0 - 4 Female BAU
## 6 Yes or no dental defect 0 - 4 Female BAU
## 7 Dioxin recommendation tolerable daily intake Undefined Female BAU
## 8 Dioxin recommendation tolerable daily intake 2018 Undefined Female BAU
## 9 Sperm concentration 0 - 4 Male BAU
## 10 Yes or no dental defect 0 - 4 Male BAU
## 11 Dioxin recommendation tolerable daily intake Undefined Male BAU
## 12 Dioxin recommendation tolerable daily intake 2018 Undefined Male BAU
## 13 PFAS TWI Undefined Female BAU
## 14 PFAS TWI Undefined Male BAU
## 15 Vitamin D recommendation Undefined Female BAU
## 16 Vitamin D recommendation Undefined Male BAU
## 17 All-cause mortality 0 - 4 Female BAU
## 18 Depression 0 - 4 Female BAU
## 19 All-cause mortality 0 - 4 Male BAU
## 20 Depression 0 - 4 Male BAU
## 21 CHD2 mortality 0 - 4 Female BAU
## 22 CHD2 mortality 0 - 4 Male BAU
## Exposure_agent mean
## 1 DHA -1.788072e+01
## 2 DHA -1.871641e+01
## 3 MeHg 1.925616e+03
## 4 MeHg 2.015614e+03
## 5 TEQ 1.875600e+01
## 6 TEQ 1.043562e+01
## 7 TEQ -2.501773e+03
## 8 TEQ -1.911467e+03
## 9 TEQ 1.963260e+01
## 10 TEQ 1.092335e+01
## 11 TEQ -2.440264e+03
## 12 TEQ -1.864471e+03
## 13 PFAS 0.000000e+00
## 14 PFAS 0.000000e+00
## 15 Vitamin D 6.184157e+02
## 16 Vitamin D 6.032113e+02
## 17 Fish -9.294861e+00
## 18 Fish -2.336224e-03
## 19 Fish -1.142399e+01
## 20 Fish -2.070744e-03
## 21 Omega3 -1.820771e-01
## 22 Omega3 -1.976604e-01oprint(summary(case_burden,"mean"))## Response mean
## 1 Dioxin recommendation tolerable daily intake 0.001004988
## 2 Dioxin recommendation tolerable daily intake 2018 0.001004988
## 3 Loss in child's IQ points 0.110000000
## 4 PFAS TWI 0.001004988
## 5 Sperm concentration 2.500000000
## 6 Vitamin D recommendation 0.001004988
## 7 Yes or no dental defect 0.060000000oprint(summary(conc,"mean"))## Fish Compound mean
## 1 Average fish ALA 0.690000
## 2 Bream ALA 0.220000
## 3 Herring ALA 1.740000
## 4 Pike ALA 0.080000
## 5 Rainbow trout ALA 4.810000
## 6 Roach ALA 0.100000
## 7 Salmon ALA 7.960000
## 8 Vendace ALA 1.350000
## 9 Whitefish ALA 2.220000
## 10 Average fish DHA 2.540000
## 11 Bream DHA 2.730000
## 12 Herring DHA 5.860000
## 13 Pike DHA 0.300000
## 14 Rainbow trout DHA 7.570000
## 15 Roach DHA 2.870000
## 16 Salmon DHA 6.690000
## 17 Vendace DHA 3.000000
## 18 Whitefish DHA 3.940000
## 19 Average fish Fish 1.000000
## 20 Bream Fish 1.000000
## 21 Herring Fish 1.000000
## 22 Pike Fish 1.000000
## 23 Rainbow trout Fish 1.000000
## 24 Roach Fish 1.000000
## 25 Salmon Fish 1.000000
## 26 Vendace Fish 1.000000
## 27 Whitefish Fish 1.000000
## 28 Average fish Omega3 7.000000
## 29 Bream Omega3 6.000000
## 30 Herring Omega3 24.000000
## 31 Pike Omega3 0.500000
## 32 Rainbow trout Omega3 18.000000
## 33 Roach Omega3 5.000000
## 34 Salmon Omega3 23.000000
## 35 Vendace Omega3 10.000000
## 36 Whitefish Omega3 10.000000
## 37 Bream PFAS 4.950000
## 38 Eel PFAS 7.895000
## 39 Herring PFAS 1.655000
## 40 Perch PFAS 8.793125
## 41 Pike-perch PFAS 2.595000
## 42 Average fish Vitamin D 0.105000
## 43 Bream Vitamin D 0.140000
## 44 Herring Vitamin D 0.156000
## 45 Pike Vitamin D 0.021000
## 46 Rainbow trout Vitamin D 0.051000
## 47 Roach Vitamin D 0.100000
## 48 Salmon Vitamin D 0.067000
## 49 Vendace Vitamin D 0.094000
## 50 Whitefish Vitamin D 0.144000oprint(summary(dose,"mean"))## Scaling mean
## 1 BW 0.01428571
## 2 Log10 0.00000000
## 3 None 1.00000000oprint(summary(ERF,"mean"))## Exposure_agent Response
## 1 Fish All-cause mortality
## 2 Omega3 Breast cancer
## 3 Fish Depression
## 4 DHA Loss in child's IQ points
## 5 TEQ Sperm concentration
## 6 TEQ Yes or no dental defect
## 7 Omega3 CHD2 mortality
## 8 Vitamin D Vitamin D recommendation
## 9 MeHg Loss in child's IQ points
## 10 PFAS Immunosuppression
## 11 PFAS PFAS TWI
## 12 TEQ Cancer morbidity
## 13 TEQ Dioxin recommendation tolerable daily intake
## 14 TEQ Dioxin recommendation tolerable daily intake 2018
## 15 Fish All-cause mortality
## 16 Omega3 Breast cancer
## 17 Fish Depression
## 18 DHA Loss in child's IQ points
## 19 TEQ Sperm concentration
## 20 TEQ Yes or no dental defect
## 21 Omega3 CHD2 mortality
## 22 Vitamin D Vitamin D recommendation
## 23 MeHg Loss in child's IQ points
## 24 PFAS Immunosuppression
## 25 PFAS PFAS TWI
## 26 TEQ Cancer morbidity
## 27 TEQ Dioxin recommendation tolerable daily intake
## 28 TEQ Dioxin recommendation tolerable daily intake 2018
## ER_function Scaling Observation mean
## 1 RR None ERF 0.997871700
## 2 RR None ERF 0.999487200
## 3 RR None ERF 0.994690400
## 4 ERS None ERF -0.001300000
## 5 ERS None ERF 0.000060000
## 6 ERS None ERF 0.001390971
## 7 Relative Hill None ERF -0.170000000
## 8 Step None ERF 100.000000000
## 9 ERS BW ERF 9.800000000
## 10 ERS BW ERF 0.022700000
## 11 TWI BW ERF 4.400000000
## 12 CSF BW ERF 0.000500000
## 13 TDI BW ERF 2.000000000
## 14 TDI BW ERF 0.288900000
## 15 RR None Threshold 0.000000000
## 16 RR None Threshold 0.000000000
## 17 RR None Threshold 0.000000000
## 18 ERS None Threshold 0.000000000
## 19 ERS None Threshold 0.000000000
## 20 ERS None Threshold 0.000000000
## 21 Relative Hill None Threshold 47.000000000
## 22 Step None Threshold 10.000000000
## 23 ERS BW Threshold 0.000000000
## 24 ERS BW Threshold 0.000000000
## 25 TWI BW Threshold 0.000000000
## 26 CSF BW Threshold 0.000000000
## 27 TDI BW Threshold 0.000000000
## 28 TDI BW Threshold 0.000000000oprint(summary(expo_dir,"mean"))## Fish Compound Kala Scenario Age
## 1 Whitefish ALA Kasvatettu BAU Female 18-45
## 2 Whitefish DHA Kasvatettu BAU Female 18-45
## 3 Whitefish Fish Kasvatettu BAU Female 18-45
## 4 Whitefish Omega3 Kasvatettu BAU Female 18-45
## 5 Whitefish Vitamin D Kasvatettu BAU Female 18-45
## 6 Average fish ALA Kaupallinen BAU Female 18-45
## 7 Average fish DHA Kaupallinen BAU Female 18-45
## 8 Average fish Fish Kaupallinen BAU Female 18-45
## 9 Average fish Omega3 Kaupallinen BAU Female 18-45
## 10 Average fish PFAS Kaupallinen BAU Female 18-45
## 11 Average fish Vitamin D Kaupallinen BAU Female 18-45
## 12 Rainbow trout ALA Kirjolohi BAU Female 18-45
## 13 Rainbow trout DHA Kirjolohi BAU Female 18-45
## 14 Rainbow trout Fish Kirjolohi BAU Female 18-45
## 15 Rainbow trout Omega3 Kirjolohi BAU Female 18-45
## 16 Rainbow trout Vitamin D Kirjolohi BAU Female 18-45
## 17 Average fish ALA Muu tuonti BAU Female 18-45
## 18 Average fish DHA Muu tuonti BAU Female 18-45
## 19 Average fish Fish Muu tuonti BAU Female 18-45
## 20 Average fish Omega3 Muu tuonti BAU Female 18-45
## 21 Average fish PFAS Muu tuonti BAU Female 18-45
## 22 Average fish Vitamin D Muu tuonti BAU Female 18-45
## 23 Herring ALA Silakka BAU Female 18-45
## 24 Herring DHA Silakka BAU Female 18-45
## 25 Herring Fish Silakka BAU Female 18-45
## 26 Herring Omega3 Silakka BAU Female 18-45
## 27 Herring PFAS Silakka BAU Female 18-45
## 28 Herring Vitamin D Silakka BAU Female 18-45
## 29 Rainbow trout ALA Tuontikirjolohi BAU Female 18-45
## 30 Rainbow trout DHA Tuontikirjolohi BAU Female 18-45
## 31 Rainbow trout Fish Tuontikirjolohi BAU Female 18-45
## 32 Rainbow trout Omega3 Tuontikirjolohi BAU Female 18-45
## 33 Rainbow trout Vitamin D Tuontikirjolohi BAU Female 18-45
## 34 Salmon ALA Tuontilohi BAU Female 18-45
## 35 Salmon DHA Tuontilohi BAU Female 18-45
## 36 Salmon Fish Tuontilohi BAU Female 18-45
## 37 Salmon Omega3 Tuontilohi BAU Female 18-45
## 38 Salmon Vitamin D Tuontilohi BAU Female 18-45
## 39 Average fish ALA Vapaa-ajan BAU Female 18-45
## 40 Average fish DHA Vapaa-ajan BAU Female 18-45
## 41 Average fish Fish Vapaa-ajan BAU Female 18-45
## 42 Average fish Omega3 Vapaa-ajan BAU Female 18-45
## 43 Average fish PFAS Vapaa-ajan BAU Female 18-45
## 44 Average fish Vitamin D Vapaa-ajan BAU Female 18-45
## 45 Whitefish ALA Kasvatettu BAU Female 45+
## 46 Whitefish DHA Kasvatettu BAU Female 45+
## 47 Whitefish Fish Kasvatettu BAU Female 45+
## 48 Whitefish Omega3 Kasvatettu BAU Female 45+
## 49 Whitefish Vitamin D Kasvatettu BAU Female 45+
## 50 Average fish ALA Kaupallinen BAU Female 45+
## 51 Average fish DHA Kaupallinen BAU Female 45+
## 52 Average fish Fish Kaupallinen BAU Female 45+
## 53 Average fish Omega3 Kaupallinen BAU Female 45+
## 54 Average fish PFAS Kaupallinen BAU Female 45+
## 55 Average fish Vitamin D Kaupallinen BAU Female 45+
## 56 Rainbow trout ALA Kirjolohi BAU Female 45+
## 57 Rainbow trout DHA Kirjolohi BAU Female 45+
## 58 Rainbow trout Fish Kirjolohi BAU Female 45+
## 59 Rainbow trout Omega3 Kirjolohi BAU Female 45+
## 60 Rainbow trout Vitamin D Kirjolohi BAU Female 45+
## 61 Average fish ALA Muu tuonti BAU Female 45+
## 62 Average fish DHA Muu tuonti BAU Female 45+
## 63 Average fish Fish Muu tuonti BAU Female 45+
## 64 Average fish Omega3 Muu tuonti BAU Female 45+
## 65 Average fish PFAS Muu tuonti BAU Female 45+
## 66 Average fish Vitamin D Muu tuonti BAU Female 45+
## 67 Herring ALA Silakka BAU Female 45+
## 68 Herring DHA Silakka BAU Female 45+
## 69 Herring Fish Silakka BAU Female 45+
## 70 Herring Omega3 Silakka BAU Female 45+
## 71 Herring PFAS Silakka BAU Female 45+
## 72 Herring Vitamin D Silakka BAU Female 45+
## 73 Rainbow trout ALA Tuontikirjolohi BAU Female 45+
## 74 Rainbow trout DHA Tuontikirjolohi BAU Female 45+
## 75 Rainbow trout Fish Tuontikirjolohi BAU Female 45+
## 76 Rainbow trout Omega3 Tuontikirjolohi BAU Female 45+
## 77 Rainbow trout Vitamin D Tuontikirjolohi BAU Female 45+
## 78 Salmon ALA Tuontilohi BAU Female 45+
## 79 Salmon DHA Tuontilohi BAU Female 45+
## 80 Salmon Fish Tuontilohi BAU Female 45+
## 81 Salmon Omega3 Tuontilohi BAU Female 45+
## 82 Salmon Vitamin D Tuontilohi BAU Female 45+
## 83 Average fish ALA Vapaa-ajan BAU Female 45+
## 84 Average fish DHA Vapaa-ajan BAU Female 45+
## 85 Average fish Fish Vapaa-ajan BAU Female 45+
## 86 Average fish Omega3 Vapaa-ajan BAU Female 45+
## 87 Average fish PFAS Vapaa-ajan BAU Female 45+
## 88 Average fish Vitamin D Vapaa-ajan BAU Female 45+
## 89 Whitefish ALA Kasvatettu BAU Non Female 18-45
## 90 Whitefish DHA Kasvatettu BAU Non Female 18-45
## 91 Whitefish Fish Kasvatettu BAU Non Female 18-45
## 92 Whitefish Omega3 Kasvatettu BAU Non Female 18-45
## 93 Whitefish Vitamin D Kasvatettu BAU Non Female 18-45
## 94 Average fish ALA Kaupallinen BAU Non Female 18-45
## 95 Average fish DHA Kaupallinen BAU Non Female 18-45
## 96 Average fish Fish Kaupallinen BAU Non Female 18-45
## 97 Average fish Omega3 Kaupallinen BAU Non Female 18-45
## 98 Average fish PFAS Kaupallinen BAU Non Female 18-45
## 99 Average fish Vitamin D Kaupallinen BAU Non Female 18-45
## 100 Rainbow trout ALA Kirjolohi BAU Non Female 18-45
## 101 Rainbow trout DHA Kirjolohi BAU Non Female 18-45
## 102 Rainbow trout Fish Kirjolohi BAU Non Female 18-45
## 103 Rainbow trout Omega3 Kirjolohi BAU Non Female 18-45
## 104 Rainbow trout Vitamin D Kirjolohi BAU Non Female 18-45
## 105 Average fish ALA Muu tuonti BAU Non Female 18-45
## 106 Average fish DHA Muu tuonti BAU Non Female 18-45
## 107 Average fish Fish Muu tuonti BAU Non Female 18-45
## 108 Average fish Omega3 Muu tuonti BAU Non Female 18-45
## 109 Average fish PFAS Muu tuonti BAU Non Female 18-45
## 110 Average fish Vitamin D Muu tuonti BAU Non Female 18-45
## 111 Herring ALA Silakka BAU Non Female 18-45
## 112 Herring DHA Silakka BAU Non Female 18-45
## 113 Herring Fish Silakka BAU Non Female 18-45
## 114 Herring Omega3 Silakka BAU Non Female 18-45
## 115 Herring PFAS Silakka BAU Non Female 18-45
## 116 Herring Vitamin D Silakka BAU Non Female 18-45
## 117 Rainbow trout ALA Tuontikirjolohi BAU Non Female 18-45
## 118 Rainbow trout DHA Tuontikirjolohi BAU Non Female 18-45
## 119 Rainbow trout Fish Tuontikirjolohi BAU Non Female 18-45
## 120 Rainbow trout Omega3 Tuontikirjolohi BAU Non Female 18-45
## 121 Rainbow trout Vitamin D Tuontikirjolohi BAU Non Female 18-45
## 122 Salmon ALA Tuontilohi BAU Non Female 18-45
## 123 Salmon DHA Tuontilohi BAU Non Female 18-45
## 124 Salmon Fish Tuontilohi BAU Non Female 18-45
## 125 Salmon Omega3 Tuontilohi BAU Non Female 18-45
## 126 Salmon Vitamin D Tuontilohi BAU Non Female 18-45
## 127 Average fish ALA Vapaa-ajan BAU Non Female 18-45
## 128 Average fish DHA Vapaa-ajan BAU Non Female 18-45
## 129 Average fish Fish Vapaa-ajan BAU Non Female 18-45
## 130 Average fish Omega3 Vapaa-ajan BAU Non Female 18-45
## 131 Average fish PFAS Vapaa-ajan BAU Non Female 18-45
## 132 Average fish Vitamin D Vapaa-ajan BAU Non Female 18-45
## 133 Whitefish ALA Kasvatettu BAU Non Female 45+
## 134 Whitefish DHA Kasvatettu BAU Non Female 45+
## 135 Whitefish Fish Kasvatettu BAU Non Female 45+
## 136 Whitefish Omega3 Kasvatettu BAU Non Female 45+
## 137 Whitefish Vitamin D Kasvatettu BAU Non Female 45+
## 138 Average fish ALA Kaupallinen BAU Non Female 45+
## 139 Average fish DHA Kaupallinen BAU Non Female 45+
## 140 Average fish Fish Kaupallinen BAU Non Female 45+
## 141 Average fish Omega3 Kaupallinen BAU Non Female 45+
## 142 Average fish PFAS Kaupallinen BAU Non Female 45+
## 143 Average fish Vitamin D Kaupallinen BAU Non Female 45+
## 144 Rainbow trout ALA Kirjolohi BAU Non Female 45+
## 145 Rainbow trout DHA Kirjolohi BAU Non Female 45+
## 146 Rainbow trout Fish Kirjolohi BAU Non Female 45+
## 147 Rainbow trout Omega3 Kirjolohi BAU Non Female 45+
## 148 Rainbow trout Vitamin D Kirjolohi BAU Non Female 45+
## 149 Average fish ALA Muu tuonti BAU Non Female 45+
## 150 Average fish DHA Muu tuonti BAU Non Female 45+
## 151 Average fish Fish Muu tuonti BAU Non Female 45+
## 152 Average fish Omega3 Muu tuonti BAU Non Female 45+
## 153 Average fish PFAS Muu tuonti BAU Non Female 45+
## 154 Average fish Vitamin D Muu tuonti BAU Non Female 45+
## 155 Herring ALA Silakka BAU Non Female 45+
## 156 Herring DHA Silakka BAU Non Female 45+
## 157 Herring Fish Silakka BAU Non Female 45+
## 158 Herring Omega3 Silakka BAU Non Female 45+
## 159 Herring PFAS Silakka BAU Non Female 45+
## 160 Herring Vitamin D Silakka BAU Non Female 45+
## 161 Rainbow trout ALA Tuontikirjolohi BAU Non Female 45+
## 162 Rainbow trout DHA Tuontikirjolohi BAU Non Female 45+
## 163 Rainbow trout Fish Tuontikirjolohi BAU Non Female 45+
## 164 Rainbow trout Omega3 Tuontikirjolohi BAU Non Female 45+
## 165 Rainbow trout Vitamin D Tuontikirjolohi BAU Non Female 45+
## 166 Salmon ALA Tuontilohi BAU Non Female 45+
## 167 Salmon DHA Tuontilohi BAU Non Female 45+
## 168 Salmon Fish Tuontilohi BAU Non Female 45+
## 169 Salmon Omega3 Tuontilohi BAU Non Female 45+
## 170 Salmon Vitamin D Tuontilohi BAU Non Female 45+
## 171 Average fish ALA Vapaa-ajan BAU Non Female 45+
## 172 Average fish DHA Vapaa-ajan BAU Non Female 45+
## 173 Average fish Fish Vapaa-ajan BAU Non Female 45+
## 174 Average fish Omega3 Vapaa-ajan BAU Non Female 45+
## 175 Average fish PFAS Vapaa-ajan BAU Non Female 45+
## 176 Average fish Vitamin D Vapaa-ajan BAU Non Female 45+
## mean
## 1 0.29916466
## 2 0.53094990
## 3 0.13475886
## 4 1.34758857
## 5 0.01940528
## 6 0.49591260
## 7 1.82553332
## 8 0.71871391
## 9 5.03099734
## 10 4.61968337
## 11 0.07546496
## 12 7.23812283
## 13 11.39139082
## 14 1.50480724
## 15 27.08653035
## 16 0.07674517
## 17 4.33923521
## 18 15.97341657
## 19 6.28874668
## 20 44.02122677
## 21 40.42222945
## 22 0.66031840
## 23 0.62528110
## 24 2.10583175
## 25 0.35935695
## 26 8.62456688
## 27 0.59473576
## 28 0.05605968
## 29 5.18552083
## 30 8.16099641
## 31 1.07807086
## 32 19.40527547
## 33 0.05498161
## 34 42.19209987
## 35 35.46044575
## 36 5.30051506
## 37 121.91184637
## 38 0.35513451
## 39 1.53422959
## 40 5.64774372
## 41 2.22352115
## 42 15.56464804
## 43 14.29214541
## 44 0.23346972
## 45 0.74791166
## 46 1.32737475
## 47 0.33689714
## 48 3.36897144
## 49 0.04851319
## 50 1.23978149
## 51 4.56383331
## 52 1.79678477
## 53 12.57749336
## 54 11.54920841
## 55 0.18866240
## 56 18.09530708
## 57 28.47847705
## 58 3.76201810
## 59 67.71632587
## 60 0.19186292
## 61 10.84808802
## 62 39.93354142
## 63 15.72186670
## 64 110.05306692
## 65 101.05557362
## 66 1.65079600
## 67 1.56320275
## 68 5.26457936
## 69 0.89839238
## 70 21.56141719
## 71 1.48683939
## 72 0.14014921
## 73 12.96380209
## 74 20.40249102
## 75 2.69517715
## 76 48.51318868
## 77 0.13745403
## 78 105.48024969
## 79 88.65111437
## 80 13.25128765
## 81 304.77961593
## 82 0.88783627
## 83 3.83557398
## 84 14.11935929
## 85 5.55880287
## 86 38.91162009
## 87 35.73036353
## 88 0.58367430
## 89 0.59832933
## 90 1.06189980
## 91 0.26951771
## 92 2.69517715
## 93 0.03881055
## 94 0.99182519
## 95 3.65106664
## 96 1.43742781
## 97 10.06199469
## 98 9.23936673
## 99 0.15092992
## 100 14.47624566
## 101 22.78278164
## 102 3.00961448
## 103 54.17306069
## 104 0.15349034
## 105 8.67847042
## 106 31.94683314
## 107 12.57749336
## 108 88.04245353
## 109 80.84445889
## 110 1.32063680
## 111 1.25056220
## 112 4.21166349
## 113 0.71871391
## 114 17.24913375
## 115 1.18947152
## 116 0.11211937
## 117 10.37104167
## 118 16.32199281
## 119 2.15614172
## 120 38.81055094
## 121 0.10996323
## 122 84.38419975
## 123 70.92089150
## 124 10.60103012
## 125 243.82369274
## 126 0.71026902
## 127 3.06845918
## 128 11.29548743
## 129 4.44704230
## 130 31.12929607
## 131 28.58429082
## 132 0.46693944
## 133 0.99721555
## 134 1.76983299
## 135 0.44919619
## 136 4.49196191
## 137 0.06468425
## 138 1.65304198
## 139 6.08511107
## 140 2.39571302
## 141 16.76999115
## 142 15.39894455
## 143 0.25154987
## 144 24.12707611
## 145 37.97130273
## 146 5.01602414
## 147 90.28843449
## 148 0.25581723
## 149 14.46411737
## 150 53.24472190
## 151 20.96248894
## 152 146.73742255
## 153 134.74076482
## 154 2.20106134
## 155 2.08427033
## 156 7.01943915
## 157 1.19785651
## 158 28.74855626
## 159 1.98245253
## 160 0.18686562
## 161 17.28506945
## 162 27.20332136
## 163 3.59356953
## 164 64.68425157
## 165 0.18327205
## 166 140.64033291
## 167 118.20148583
## 168 17.66838353
## 169 406.37282123
## 170 1.18378170
## 171 5.11409864
## 172 18.82581239
## 173 7.41173716
## 174 51.88216012
## 175 47.64048471
## 176 0.77823240#oprint(summary(exposure,"mean"))
oprint(summary(fish_proportion,"mean"))## Age mean
## 1 Female 18-45 0.4528302
## 2 Female 45+ 1.1320755
## 3 Non Female 18-45 0.9056604
## 4 Non Female 45+ 1.5094340oprint(summary(incidence,"mean"))## Response Age Adjust mean
## 1 Loss in child's IQ points 0 - 4 BAU 1.1920
## 2 Sperm concentration 0 - 4 BAU 0.0140
## 3 Yes or no dental defect 0 - 4 BAU 0.0448
## 4 Dioxin recommendation tolerable daily intake Undefined BAU 0.1100
## 5 Dioxin recommendation tolerable daily intake 2018 Undefined BAU 0.3200
## 6 PFAS TWI Undefined BAU 1.0000
## 7 Vitamin D recommendation Undefined BAU 0.2200oprint(summary(PAF,"mean"))## Exposure_agent Response Age
## 1 DHA Loss in child's IQ points 0 - 4
## 2 MeHg Loss in child's IQ points 0 - 4
## 3 TEQ Sperm concentration 0 - 4
## 4 TEQ Yes or no dental defect 0 - 4
## 5 Fish All-cause mortality 0 - 4
## 6 Omega3 Breast cancer 0 - 4
## 7 Fish Depression 0 - 4
## 8 Omega3 CHD2 mortality 0 - 4
## 9 TEQ Dioxin recommendation tolerable daily intake Undefined
## 10 TEQ Dioxin recommendation tolerable daily intake 2018 Undefined
## 11 Vitamin D Vitamin D recommendation Undefined
## 12 PFAS PFAS TWI Undefined
## 13 Fish All-cause mortality Undefined
## 14 Omega3 Breast cancer Undefined
## 15 Fish Depression Undefined
## 16 Omega3 CHD2 mortality Undefined
## Adjust mean
## 1 BAU -0.001090604
## 2 BAU 0.117449664
## 3 BAU 0.004285714
## 4 BAU 0.031048457
## 5 BAU -0.002128300
## 6 BAU -0.000512800
## 7 BAU -0.005309600
## 8 BAU -0.003541667
## 9 BAU -8.090909091
## 10 BAU -2.125000000
## 11 BAU 1.000000000
## 12 BAU 0.000000000
## 13 BAU -0.002128300
## 14 BAU -0.000512800
## 15 BAU -0.005309600
## 16 BAU -0.003541667oprint(summary(population,"mean"))## Gender Age mean
## 1 Female 0 - 4 125040
## 2 Male 0 - 4 130884
## 3 Female 10 - 14 151113
## 4 Male 10 - 14 157712
## 5 Female 15 - 19 144441
## 6 Male 15 - 19 152230
## 7 Female 20 - 24 152265
## 8 Male 20 - 24 161679
## 9 Female 25 - 29 172593
## 10 Male 25 - 29 183092
## 11 Female 30 - 34 169653
## 12 Male 30 - 34 181115
## 13 Female 35 - 39 174660
## 14 Male 35 - 39 186122
## 15 Female 40 - 44 168547
## 16 Male 40 - 44 177928
## 17 Female 45 - 49 154391
## 18 Male 45 - 49 159982
## 19 Female 5 - 9 149633
## 20 Male 5 - 9 156654
## 21 Female 50 - 54 176612
## 22 Male 50 - 54 179182
## 23 Female 55 - 59 185152
## 24 Male 55 - 59 183719
## 25 Female 60 - 64 183336
## 26 Male 60 - 64 176283
## 27 Female 65 - 69 185685
## 28 Male 65 - 69 171275
## 29 Female 70 - 74 186034
## 30 Male 70 - 74 163697
## 31 Female 75 - 79 118190
## 32 Male 75 - 79 93987
## 33 Female 80 - 84 96256
## 34 Male 80 - 84 65140
## 35 Female 85+ 103429
## 36 Male 85+ 47581
## 37 Female Undefined 2797030
## 38 Male Undefined 2728262oprint(summary(RR,"mean"))## Exposure_agent Response ER_function Scaling mean
## 1 Fish All-cause mortality RR None 0.9978717
## 2 Omega3 Breast cancer RR None 0.9994872
## 3 Fish Depression RR None 0.9946904
## 4 Omega3 CHD2 mortality Relative Hill None 0.9964583###################
# Graphs
trim <- function(ova) return(oapply(ova, NULL, mean, "Iter")@output)
plot_ly(trim(amount), x=~Scenario, y=~amountResult, color=~Kala, type="bar") %>%
layout(yaxis=list(title="Kalan kokonaiskulutus Suomessa (milj kg /a)"), barmode="stack")## Warning: `arrange_()` is deprecated as of dplyr 0.7.0.
## Please use `arrange()` instead.
## See vignette('programming') for more help
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.plot_ly(trim(conc_vit), x=~Nutrient, y=~conc_vitResult, color=~Kala, type="scatter", mode="markers") %>%
layout(yaxis=list(title="Concentrations of nutrients (mg or ug /g)"))## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colorstmp <- exposure / Ovariable(
output = data.frame(
Exposure_agent = c("Fish","Vitamin D", "Omega3", "ALA", "DHA", "TEQ", "PFAS"),
Result = c(1, 1, 1000, 1000, 1000, 1, 1)
),
marginal = c(TRUE, FALSE)
)
plot_ly(trim(tmp), x=~exposureSource, y=~Result, color=~Exposure_agent, text=~Exposure_agent, type="bar") %>%
layout(yaxis=list(title="Exposure to nutrients (g or ug /d)"))cat("Kalaperäisiä tautitaakkoja Suomessa\n")## Kalaperäisiä tautitaakkoja Suomessaif(openv$N>1) {
tmp <- summary(oapply(BoDattr,NULL,sum,c("Age","Gender","Response")))
tmp <- data.frame(
Altiste = tmp$Exposure_agent,
Vaikutus = tmp$Response,
Keskiarvo = as.character(signif(tmp$mean,2)),
"95 luottamusväli" = paste0(signif(tmp$Q0.025,2)," - ", signif(tmp$Q0.975,2)),
Keskihajonta = signif(tmp$sd,2)
)#[rev(match(lev, tmp$Exposure_agent)),]
oprint(tmp)
tmp <- summary(oapply(BoDattr,NULL,sum,c("Age","Gender","Exposure_agent")))
tmp <- data.frame(
Terveysvaikutus = tmp$Response,
Keskiarvo = signif(tmp$mean,2),
"95 luottamusväli" = paste0(signif(tmp$Q0.025,2)," - ", signif(tmp$Q0.975,2)),
Keskihajonta = signif(tmp$sd,2)
)
oprint(tmp)
}
ggplot(trim(BoDattr), aes(x=Exposure_agent, weight=BoDattrResult, fill=Response))+geom_bar()
ggplot(trim(BoDattr), aes(x=Response, weight=BoDattrResult, fill=Exposure_agent))+geom_bar()
plot_ly(trim(BoDattr), x=~Exposure_agent, y=~BoDattrResult, color=~Response, text=~paste(Age, Exposure_agent, sep=": "), type="bar") %>%
layout(yaxis=list(title="Disease burden (DALY /a); CHD2=coronary heart disease"), barmode="stack")## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors
## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors################ Insight network
gr <- scrape(type="assessment")
objects.latest("Op_en3861", "makeGraph") # [[Insight network]]## Loading objects:
## makeGraphgr <- makeGraph(gr)## Loading required package: DiagrammeR## Loading objects:
## formatted
## Loading objects:
## chooseGr#export_graph(gr, "ruori.svg")
#render_graph(gr) # Does not work: Error in generate_dot(graph) : object 'attribute' not found
##################### Diagnostics
objects.latest("Op_en6007", code_name="diagnostics")## Loading objects:
## showind
## binoptest
## showLoctable
## ovashapetestshowLoctable()showind()## subgrouping is not an ovariable.
## sumExposcen is not an ovariable.
## mc2d is not an ovariable.
## mc2dparam is not an ovariable.